Magnetic Resonance (MR) imaging often applies thermometry for the purpose of three-dimensional temperature imaging, and for monitoring local specific absorption rate (SAR) to evaluate thermal risk in regular MRI scans, and for monitoring thermal therapy. More specifically, thermal therapy is often used for tumor ablation in cancer treatment including cancers of the breast, prostate, liver, kidney, and brain. In each case, ablation is dependent on reliable volumetric measurement of temperature to guide heating which may be accomplished with MR imaging. However, the method is susceptible to drift in the imaging magnetic field as well as patient motion, both of which must be accounted for and corrected to improve efficacy of the treatment.
Specifically, thermal changes in substances undergoing MR imaging or Nuclear Magnetic Resonance (NMR) spectroscopy are known to cause spin resonance frequency shifts. Phase difference MR imaging techniques have been used to monitor temperature changes in tissues in vivo by measuring the temperature dependent spin resonance frequency shift. The phase difference technique typically only applies to aqueous (water based) tissue and not to adipose (fat) tissue. In adipose (fat) tissue, temperature induced spin resonance frequency shifts are minor when compared with the spin resonance frequency shift seen in aqueous tissue. Therefore, in anatomies containing both water and fat, the fat tissue can cause significant error in the phase difference MR temperature measurements. The error is further enhanced in imaging tissues with varying water and fat content such as breast tissue.
One technique commonly used in phase difference temperature mapping is shown in FIG. 1 and involves acquiring two complex images with the same echo-time (τ). As shown, one gradient echo (GRE) image 10 is taken at time (ta). A second GRE image 15 is taken at a later time (tb). The temperature change between time ta and tb is computed by taking the phase difference 20 of the two images and dividing the resulting image by the appropriate scaling constants 25, such as by the scaling term, αγB0τ, where τ is the GRE image echo time, B0 is the main magnetic field strength, γ is the gyromagnetic ratio of the proton, and α is the temperature dependent shift coefficient (α=−0.01 ppm/° C. for water). One method to compute the phase difference 20 is to compute the phase of image 10 and image 15 separately. These separate phase quantities are then subtracted to compute the phase difference. (The phase of complex image 10 and 15 can be computed by taking the arctangent of the imaginary component divided by the real component on a pixel-by-pixel basis.) Another method for computing the phase difference 20 is to multiply image 15 by the complex conjugate (*) of image 10 on a pixel-by-pixel basis. Taking the argument (Arg) of this complex quantity give the phase difference 20. This temperature measurement technique works because the phase of imaged aqueous tissue (muscle, tumor, etc.) changes with temperature. The temperature change map 30 is generated from the processing.
This technique can only measure temperature change in aqueous or water based tissue. In imaged fat, the phase does not change with temperature. In tissue containing both fat and aqueous tissue, the temperature change map accuracy is affected by the presence of fat. Additionally, this technique is not accurate when phase disturbances exist: magnetic field (B0) drift, patient motion, and breathing. Thus, the accuracy of this technique is affected by the presence of fat and time varying phase disturbances.
A second common technique is shown in FIG. 2 using a fat-referenced phase difference temperature mapping. The technique improves upon the two key limitations of the technique illustrated in FIG. 1, namely the technique is less affected by the presence of fat and phase disturbances. In FIG. 2, separate fat images 55, 56 and water images 50, 52 are acquired, wherein the water images 50, 52 at two different measurement points (ta and tb) experience phase change due to both the temperature induced phase change and non-temperature dependent phase change. The phase change of the fat images, 55 and 56, at two different measurement points (ta and tb) is due to the non-temperature dependent phase change. The FIG. 2 technique uses the fat signal, 55 and 56, to correct for non-temperature dependent phase disturbances. The phase difference 60 of the water image 50 and fat image 55 at measurement point ta is calculated and the phase difference 62 of the water image 52 and fat image 56 at measurement point tb is calculated. The phase difference measurements, 60 and 62, are processed by the summer 65, which subtracts the phase difference of water and fat taken at measurement point ta from the phase difference of water and fat taken at measurement point tb. The output of the summer operator 65 is scaled 25, such as by the scaling term, αγB0τ. From the scaling 25, the temperature change map is generated 70.
Accurate and complete fat water separation is important for this technique to produce accurate temperature maps in anatomies containing fat. If separation is not complete, referenced technique will produce significant error. In one example, the present technique uses a spoiled gradient echo imaging sequence (SPGR) with frequency selective suppression pulses to obtain the separate fat and water images. As noted, time-dependent phase disturbances and main magnetic field (B0) inhomogeneity adversely affects the quality of this type of fat water separation. This technique is therefore limited in another regard because it relies on the assumption that there is a tissue component of fat and water in each imaging voxel. In the human body, fat and water tissue is generally heterogeneously distributed and a fat-reference in every imaged pixel cannot be relied upon.
Some limitations of this second technique include scenarios when time-varying phase disturbances are large or significant main magnetic field (B0) inhomogeneity exists in the imaged region, whereby accurate and complete fat-water separation is poor. This results in inaccurate temperature maps. Furthermore, the reference signal (fat) must be present in every voxel for reference correction to work.
Referring to FIG. 3, another conventional temperature mapping technique, technique 3, is depicted wherein fat-referenced temperature mapping is accomplished using an IDEAL algorithm. The IDEAL temperature mapping algorithm is used to produce the separate fat and water images used in certain fat-referenced thermometry. IDEAL is a technique that acquires multiple images (I1, I2, I3), at difference echo times (τ1, τ2, τ3) at two different time intervals ta, 105 and tb, 110. The IDEAL algorithm, 115 and 145, employs an iterative processing 120 to compute two different phase maps (ψo) from images acquired at time ta and tb 125, 140 that are used with an algorithm such as the linear least squares approach algorithm 130 to estimate the separate water, 150 and 152, and fat, 155 and 156, signal components. IDEAL, as well as the whole class of multi-echo techniques, is particularly suited for accurate fat-water separation when significant magnetic field inhomogeneity exists.
The thermometry technique of FIG. 3 differs from the technique illustrated in FIG. 2 by the way the separate water images, 150 and 152, and fat images, 155 and 156, are obtained. FIG. 3 uses multiple images, 105 and 110, and then IDEAL algorithm post-processing, 115 and 145, to obtain the reconstructed fat images, 155 and 156, and water images, 150 and 152.
Although IDEAL algorithm processing can produce completely separated water and fat magnitude images, the phase information is affected by the iterative step 120 in algorithms 115 and 145. Temperature information is contained in the phase images, and step 120 affects the accuracy of any temperature measurements obtained from IDEAL reconstructed images. In the iterative step 120, the phase map ψo(ta) 125 is computed from the 3 images 105, and the phase map ψo(tb) 140 is computed from a different 3 images 110. Once the phase map 125, 140 is processed, the water and fat images 150, 152, 155, 156 are reconstructed.
The reconstructed water and fat images 150, 152, 155, and 156 are complex valued and thus have phase components. The phase of these reconstructed images, 150 and 155, is affected by the phase map (ψo), 125 and 140, that is produced in each iterative step 120. When applying the IDEAL algorithm, 115 and 145, at time points ta and tb, two different phase maps ψo(ta) 125 and ψo(tb) 140 are used for the reconstruction of the images, namely before temperature change (ta) and after temperature change (tb). When recalculating the phase map, 125 and 140, at each measurement point, phase due to temperature change is interpreted as magnetic field inhomogeneity and is removed from the reconstructed water image 152. As a result the IDEAL algorithm loses the important phase information that is used to computed temperature change map.
Similar to the technique shown in FIG. 2, the phase difference 60 of the water image 150 and fat image 155 at measurement point ta is calculated and the phase difference 62 of the water image 152 and the fat image 156 at measurement point tb is calculated. The phase difference measurements, 60 and 62, are processed by the arithmetic operator 65. The output of the arithmetic operator 65 is scaled by the scaling factor (αγB0τ2) 125 and the corresponding temperature change map is generated 160.
The IDEAL algorithm can produce fairly accurate water and fat magnitude images under certain circumstances. However, when used for temperature mapping, IDEAL processing recalculates the phase map (ψo) at each measurement point (t), and as a result the temperature dependent phase information of the water image is lost when the phase map is recalculated.
An alternative fat-referenced configuration mapping is shown in the flow diagram in FIG. 4. Similar to the system processing of FIG. 2, the water images, 50 and 52, and fat images, 55 and 56, from two time periods ta and tb are obtained for processing. One detail of the FIG. 4 technique that deviates from the technique of FIG. 2, is that the phase difference between fat images, 55 and 56, at time points tb and ta respectively and water images, 50 and 52, at time points tb and ta respectively is computed. In addition, before the phase difference 60 of the fat images 55, 56 is subtracted from the phase difference 60 of the water images, 50 and 52, via the summer 145, the phase difference map of the reference images of the fat images, 55 and 56, is subjected to a weighted polynomial fit to a 2D surface 175. The difference between the phase difference of the water images and the polynomial fitting 175 of the phase difference fat images is calculated in the arithmetic operator 145. The output of the summer is subject to scaling 25 and the temperature change map 180 is computed.
The technique of FIG. 4 is still plagued with the key problem facing the FIG. 2 technique, namely the use of frequency selective pulses can fail to produce fat only images and water only images. As was detailed herein, this leads to significant temperature measurement errors.
As an example, the disadvantage of the FIG. 4 technique occurs when large time-varying phase disturbances are present, and/or significant main magnetic field inhomogeneity exists, wherein accurate and complete fat-water separation is poor and results in inaccurate temperature maps.
In summary, the prior techniques of FIGS. 1, 2, and 4 are unable to accurately produce water only and fat only images when time-varying phase disturbances and/or large magnetic field inhomogeneity is present in the anatomy of interest. Fat signal in the water image confounds the basic phase difference technique for the FIG. 1 technique. Fat signal in the water image introduces errors in the fat-referenced temperature mapping technique such as in the FIG. 2 and FIG. 4 technique. Also, water signal in the fat image cause temperature measurement errors for the techniques of FIGS. 2 and 4.
The FIG. 3 technique employs the IDEAL algorithm with a multi-echo acquisition fat-water separation method to obtain water only and fat only images. This technique produces good fat and water magnitude images. However, thermometry measurements depend on quality of phase images, not quality of magnitude images, and phase images produced by method shown in FIG. 3 are not adequate for accurate thermometry. IDEAL reconstructed images cannot reliably be used for thermometry because estimation of the resonance offset due to field inhomogeneity in the iterative step will remove the temperature dependent phase information from the phase of the reconstructed water image.
What are needed therefore are systems and methods that alleviate the noted disadvantages of the state of the art techniques described above.